{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tides on a simple 2D field\n",
    "\n",
    "In this notebook we will be running the [Landlab](http://landlab.github.io/#/) tidal-flow-calculator over a simple 2D field of constant depth and roughness. The domain used is a modified version of one of the examples from [this notebook](https://github.com/landlab/landlab/blob/gt/tidal-flow-component/notebooks/tutorials/tidal_flow/tidal_flow_calculator.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing and Installing\n",
    "\n",
    "First we will import some standard scientific Python libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to install some libraries (including Landlab) to properly accomplish this task.\n",
    "\n",
    "As of this writing (8/18/2020) the tidal-flow-calculator is not part of the core Landlab installation. As a consequence, we need to checkout the feature branch containing the tidal-flow-calculator component (https://github.com/landlab/landlab/tree/gt/tidal-flow-component). After checking out or cloning this branch locally, `python setup.py install` should be run to build a new landlab installation containing the tidal-flow-calculator.\n",
    "\n",
    "To simulate passive particle transport we will use the Lagrangian-based transport model [dorado](https://github.com/passaH2O/dorado). We can install dorado by typing `pip install pydorado` from the command line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from landlab.components import TidalFlowCalculator\n",
    "from landlab import RasterModelGrid\n",
    "from dorado.routines import plot_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastly there are some custom scripts containing functions we want to use for this example. These scripts are available in the same directory as this notebook, and so our imports will be happening locally."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from map_fun import gridded_vars\n",
    "from plot_fun import group_plot\n",
    "from plot_fun import plot_depth\n",
    "from particletransport import init_particles\n",
    "from particletransport import tidal_particles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Parameters\n",
    "\n",
    "We are going to create model parameters that define the tidal scenario for the tidal-flow-calculator as well as the random field properties."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we will define the size of the domain (which is going to be a rectangle) as well as the grid spacing, mean water depth, and properties associated with the tide. In this 2D domain, the left and bottom boundaries are closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "nrows = 150\n",
    "ncols = 250\n",
    "grid_spacing = 100.0  # m\n",
    "mean_depth = 2.0  # m\n",
    "tidal_range = 2.0  # m\n",
    "roughness = 0.01  # s/m^1/3, i.e., Manning's n\n",
    "tide_period = 1*60  # tidal period in seconds\n",
    "n_tide_periods = 15  # number of tidal periods to move particles around for"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining the Landlab Grid\n",
    "\n",
    "Next we are going to be defining the Landlab grid object and its associated parameters. Here the depth will be constant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create and set up the grid\n",
    "grid = RasterModelGrid((nrows, ncols), xy_spacing=grid_spacing)\n",
    "z = grid.add_zeros('topographic__elevation', at='node')\n",
    "z[:] = -mean_depth\n",
    "grid.set_closed_boundaries_at_grid_edges(False, False, True, True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Instantiate the TidalFlowCalculator and run it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the TidalFlowCalculator\n",
    "tfc = TidalFlowCalculator(grid, tidal_range=tidal_range,\n",
    "                          tidal_period=tide_period, roughness=roughness)\n",
    "\n",
    "# run it\n",
    "tfc.run_one_step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize the particles and run them\n",
    "\n",
    "Here we will specify where we want the particles to be initially placed and the number of particles to use. Then we will allow them to move with the tides."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get gridded values\n",
    "gvals = gridded_vars(grid)\n",
    "\n",
    "# initialize the particle parameters\n",
    "seed_xloc = list(range(70, 180))\n",
    "seed_yloc = list(range(50, 110))\n",
    "Np_tracer = 100  # use 100 particles\n",
    "params = init_particles(seed_xloc, seed_yloc, Np_tracer, grid_spacing, gvals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "# move the particles with the tides\n",
    "walk_data = tidal_particles(params, tide_period/10, n_tide_periods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make visualizations\n",
    "\n",
    "First we will visualize the domain, then the velocity components of the ebb and flood tides. Afterwards we will plot the particle locations at beginning and end of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jayh/miniconda3/envs/espin/lib/python3.8/site-packages/landlab/plot/imshow.py:267: MatplotlibDeprecationWarning: You are modifying the state of a globally registered colormap. In future versions, you will not be able to modify a registered colormap in-place. To remove this warning, you can make a copy of the colormap first. cmap = copy.copy(mpl.cm.get_cmap(\"YlGnBu\"))\n",
      "  cmap.set_bad(color=color_for_closed)\n",
      "/home/jayh/miniconda3/envs/espin/lib/python3.8/site-packages/landlab/plot/imshow.py:307: MatplotlibDeprecationWarning: The 'norm' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  cb = plt.colorbar(norm=norm, shrink=shrink)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize the domain\n",
    "plot_depth(grid)\n",
    "plt.title('Water Depth')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot velocity information\n",
    "group_plot(gvals)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot particle locations on the roughness field\n",
    "plt.figure(figsize=(10, 10))\n",
    "# first plot initial locations as blue dots\n",
    "plot_state(np.flipud(np.reshape(z,grid.shape)), \n",
    "           walk_data, iteration=0, target_time=None, c='b')\n",
    "# then plot final locations as red dots\n",
    "plot_state(np.flipud(np.reshape(z,grid.shape)), \n",
    "           walk_data, iteration=-1, target_time=None, c='r')\n",
    "# make the colorbar - yellow for high roughness, purple for low\n",
    "plt.colorbar()\n",
    "# tighten layout\n",
    "plt.tight_layout()\n",
    "# show it\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Animated Results\n",
    "\n",
    "While the above still image is nice, it does not totally reflect how the tides have influence the movement of the passive tracers. A better way of visualizing this is by animating the movement of the particles at each ebb and flood tide:\n",
    "\n",
    "![simple_2d_gif](imgs/demo_Simple2D.gif)\n",
    "\n",
    "With this visual we can see the oscillatory nature of the flow field and the way in which the particles move with the tides. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
